code: def jax_jit_jvp_execute(tapes, execute_fn, jpc, device):
      """Execute a batch of tapes with JAX parameters using JVP derivatives.
  
      Args:
          tapes (Sequence[.QuantumTape]): batch of tapes to execute
          execute_fn (Callable[[Sequence[.QuantumTape]], ResultBatch]): a function that turns a batch of circuits into results
          jpc (JacobianProductCalculator): a class that can compute the Jacobian for the input tapes.
          device (pennylane.Device, pennylane.devices.Device): The device used for execution. Used to determine the shapes of outputs for
              pure callback calls.
  
      Returns:
          TensorLike: A nested tuple of tape results. Each element in
          the returned tuple corresponds in order to the provided tapes.
  
      """
  
      if any(
          m.return_type
          in (qml.measurements.Counts, qml.measurements.AllCounts)
          for t in tapes
          for m in t.measurements
      ):
          # Obtaining information about the shape of the Counts measurements is
          # not implemented and is required for the callback logic
          raise NotImplementedError(
              "The JAX-JIT interface doesn't support qml.counts."
          )
  
      parameters = tuple(
          tuple(t.get_parameters(trainable_only=False)) for t in tapes
      )
  
      return _execute_jvp_jit(
          parameters,
          _NonPytreeWrapper(tuple(tapes)),
          execute_fn,
          jpc,
          device,
      )
  
file_path: pennylane/pennylane/workflow/interfaces/jax_jit.py
imports: import jax
  import jax.numpy
  import pennylane
  from pennylane.typing import ResultBatch
  from ..jacobian_products import _compute_jvps
  from .jax import _NonPytreeWrapper
label_desc: Use image node_id 12 for calling a global function with example usage: jax_jit_jvp_execute(tapes, execute_fn, jpc, device) and returns: _execute_jvp_jit